User Authentication in mobile sensors using RNN and SOA Algorithm

User Authentication in mobile sensors using RNN and SOA Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article User Authentication in mobile sensors using RNN and SOA Algorithm Vinod P R, Anitha A This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5225190/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Implicit verification systems prevent security and threat risks that demonstrate behavior for cell phones. Nevertheless, of late, experts have revealed that the presentation of social biometrics is not enough. Hence, in this paper, develop a hybrid approach for gait-based user authentication using mobile sensors. The Recurrent Neural Network (RNN) and Seagull Optimization Algorithm (SOA) are combined in the suggested hybrid approach. The best network parameters will be chosen using the SOA in order to enhance RNN performance. The principle purpose of the work is to identify a pair of class customer with a character tricky where a person who is a real customer of a cell phone is labeled authenticated, while any remaining person is designated as non-authenticated. There are four stages involved in the suggested methodology: pre-processing, feature extraction, activity recognition, and authentication. An average smoothing filter helps to remove the unwanted noise during the pre-processing stage. Twelve distinct features are extracted during the feature extraction phase. The suggested method is applied in the activity recognition stage in order to identify the activities. The probabilistic scoring model is utilized for user validation during the user authentication phase. Finally, the user validation is identified based on their activities which is executed and validated. To evaluate the performance of the proposed methodology, it is associated with the traditional techniques such as RNN-Particle Swarm Optimization (PSO) and RNN- (Genetic Algorithm). gait authentication feature extraction recurrent neural network activity recognition pre-processing and probabilistic scoring model Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5225190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469095363,"identity":"322cbabb-688a-4afc-a973-3ffbf8bf6ed0","order_by":0,"name":"Vinod P R","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACZiDmgbAOAAkJGVK0sCWAtPAQZxNEGY8Bgo0PGBznPfjg7Y578vLtPZ9f3aix4GFgP3x0A14th/mSDeeeKTbccObsNuucY0CH8aSl3cCnRbKZx0yaty0hwUAid5txDhtQiwSPGXFa5Oe/eWac848ILfzMUC0MN3iYH+e2EafFGOiXBKBf0syYc/skeNgI+YWN/4whMMQSgCF2+PHnnG91cvzsh4/h1QIGjA0Q7RJgkqByJC3MH4hSPQpGwSgYBSMOAABbMkCoK5a7DwAAAABJRU5ErkJggg==","orcid":"","institution":"Noorul Islam Centre For Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Vinod","middleName":"P","lastName":"R","suffix":""},{"id":469095364,"identity":"207e3e1d-f5eb-4183-89e5-0492d0f7fa0d","order_by":1,"name":"Anitha A","email":"","orcid":"","institution":"Noorul Islam Centre For Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Anitha","middleName":"","lastName":"A","suffix":""}],"badges":[],"createdAt":"2024-10-08 12:09:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5225190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5225190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86063036,"identity":"28fea3b9-18e7-4574-92be-5f60079743f9","added_by":"auto","created_at":"2025-07-05 09:53:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":834460,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5225190/v1_covered_3be2c152-6d93-4541-aef5-d2085e0d86be.pdf"}],"financialInterests":"","formattedTitle":"User Authentication in mobile sensors using RNN and SOA Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gait authentication, feature extraction, recurrent neural network, activity recognition, pre-processing and probabilistic scoring model","lastPublishedDoi":"10.21203/rs.3.rs-5225190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5225190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImplicit verification systems prevent security and threat risks that demonstrate behavior for cell phones. 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